Toward Fast and Accurate Vehicle Detection in Aerial Images Using Coupled Region-Based Convolutional Neural Networks
Abstract
Vehicle detection in aerial images, being an interesting but challenging problem, plays an important role for a wide range of applications. Traditional methods are based on sliding-window search and handcrafted or shallow-learning-based features with heavy computational costs and limited representation power. Recently, deep learning algorithms, especially region-based convolutional neural networks (R-CNNs), have achieved state-of-the-art detection performance in computer vision. However, several challenges limit the applications of R-CNNs in vehicle detection from aerial images: 1) vehicles in large-scale aerial images are relatively small in size, and R-CNNs have poor localization performance with small objects; 2) R-CNNs are particularly designed for detecting the bounding box of the targets without extracting attributes; 3) manual annotation is generally expensive and the available manual annotation of vehicles for training R-CNNs are not sufficient in number. To address these problems, this paper proposes a fast and accurate vehicle detection framework. On one hand, to accurately extract vehicle-like targets, we developed an accurate-vehicle-proposal-network (AVPN) based on hyper feature map which combines hierarchical feature maps that are more accurate for small object detection. On the other hand, we propose a coupled R-CNN method, which combines an AVPN and a vehicle attribute learning network to extract the vehicle's location and attributes simultaneously. For original large-scale aerial images with limited manual annotations, we use cropped image blocks for training with data augmentation to avoid overfitting. Comprehensive evaluations on the public Munich vehicle dataset and the collected vehicle dataset demonstrate the accuracy and effectiveness of the proposed method.
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Vehicles can be detected in high-resolution satellite images with high accuracy, and image counts can estimate average vehicle traffic.
“Vehicles can also be detected in high-resolution satellite images with high accuracy [177,390,566,747], and image counts can serve to estimate average vehicle traffic [405].”
From Tackling climate change with machine learning (2023) · cites this paper as [177]
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105 · see citing works
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839 · see citing works
Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2389824 · Cited by 11,971 · see citing works
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works
Caffe: Convolutional Architecture for Fast Feature Embedding
Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., Guadarrama, S., & Darrell, T. (2014) · Proceedings of the 22nd ACM international conference on Multimedia · doi:10.1145/2647868.2654889 · Cited by 11,077 · see citing works
Selective Search for Object Recognition
Uijlings, J. R. R., van de Sande, K. E. A., Gevers, T., & Smeulders, A. W. M. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0620-5 · Cited by 5,980 · see citing works
Region-based convolutional networks for accurate object detection and segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2016) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2015.2437384 · Cited by 2,912 · see citing works
Fast Feature Pyramids for Object Detection
Dollár, P., Appel, R., Belongie, S., & Perona, P. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2300479 · Cited by 1,941 · see citing works
Cheng, G., Zhou, P., & Han, J. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2601622 · Cited by 1,771 · see citing works
Hypercolumns for object segmentation and fine-grained localization
Hariharan, B., Arbelaez, P., Girshick, R., & Malik, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298642 · Cited by 1,511 · see citing works
A survey on object detection in optical remote sensing images
Cheng, G., & Han, J. (2016) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2016.03.014 · Cited by 1,503 · see citing works
Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks
Bell, S., Zitnick, C. L., Bala, K., & Girshick, R. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.314 · Cited by 1,344 · see citing works
Cheng, G., Han, J., Zhou, P., & Guo, L. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2014.10.002 · Cited by 1,007 · see citing works
HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection
Kong, T., Yao, A., Chen, Y., & Sun, F. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.98 · Cited by 934 · see citing works
Han, J., Zhang, D., Cheng, G., Guo, L., & Ren, J. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2374218 · Cited by 734 · see citing works
Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks
Chen, X., Xiang, S., Liu, C., & Pan, C. (2014) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2014.2309695 · Cited by 619 · see citing works
Saliency-Guided Unsupervised Feature Learning for Scene Classification
Zhang, F., Du, B., & Zhang, L. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2014.2357078 · Cited by 542 · see citing works
Fast Multiclass Vehicle Detection on Aerial Images
Liu, K., & Máttyus, G. (2015) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2439517 · Cited by 345 · see citing works
Weakly Supervised Learning Based on Coupled Convolutional Neural Networks for Aircraft Detection
Zhang, F., Du, B., Zhang, L., & Xu, M. (2016) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2016.2569141 · Cited by 257 · see citing works
Convolutional Neural Network Based Automatic Object Detection on Aerial Images
Ševo, I., & Avramović, A. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2016.2542358 · Cited by 229 · see citing works
Automatic Car Counting Method for Unmanned Aerial Vehicle Images
Moranduzzo, T., & Melgani, F. (2013) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2253108 · Cited by 209 · see citing works
Vehicle Detection in Aerial Surveillance Using Dynamic Bayesian Networks
Cheng, H.-Y., Weng, C.-C., & Chen, Y.-Y. (2011) · IEEE Transactions on Image Processing · doi:10.1109/tip.2011.2172798 · Cited by 200 · see citing works
Han, J., Zhou, P., Zhang, D., Cheng, G., Guo, L., Liu, Z., Bu, S., & Wu, J. (2014) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2013.12.011 · Cited by 200 · see citing works
Detecting Cars in UAV Images With a Catalog-Based Approach
Moranduzzo, T., & Melgani, F. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2296351 · Cited by 200 · see citing works
Vehicle Detection Using Partial Least Squares
Kembhavi, A., Harwood, D. M., & Davis, L. S. (2010) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.182 · Cited by 197 · see citing works
Du, B., Zhang, Y., Zhang, L., et al. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2601268 · Cited by 193 · see citing works
PVANET: Deep but Lightweight Neural Networks for Real-time Object DetectionPreprint
Kim, K.-H., Hong, S., Roh, B., Cheon, Y., & Park, M. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1608.08021 · Cited by 186 · see citing works
Efficient Saliency-Based Object Detection in Remote Sensing Images Using Deep Belief Networks
Diao, W., Sun, X., Zheng, X., Dou, F., Wang, H., & Fu, K. (2016) · IEEE Geoscience and Remote Sensing Letters · doi:10.1109/lgrs.2015.2498644 · Cited by 182 · see citing works
Semi-Supervised Multitask Learning for Scene Recognition
Lu, X., Li, X., & Mou, L. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2362959 · Cited by 179 · see citing works
Real-Time Big Data Analytical Architecture for Remote Sensing Application
Rathore, M. M., Paul, A., Ahmad, A., Chen, B.-W., Huang, B., & Ji, W. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2424683 · Cited by 177 · see citing works
Airborne Vehicle Detection in Dense Urban Areas Using HoG Features and Disparity Maps
Tuermer, S., Kurz, F., Reinartz, P. H., & Stilla, U. (2013) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2242846 · Cited by 169 · see citing works
Vehicle Detection in High-Resolution Aerial Images via Sparse Representation and Superpixels
Chen, Z., Wang, C., Wen, C., Teng, X., Chen, Y., Guan, H., Luo, H., Cao, L., & Li, J. (2015) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2015.2451002 · Cited by 115 · see citing works
An Operational System for Estimating Road Traffic Information from Aerial Images
Jens Leitloff, A., Rosenbaum, D., Kurz, F., Meynberg, O., & Reinartz, P. H. (2014) · Remote Sensing · doi:10.3390/rs61111315 · Cited by 113 · see citing works
Zhang, L., & Zhang, Y. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2620900 · Cited by 100 · see citing works
Zhou, P., Cheng, G., Liu, Z., Bu, S., & Hu, X. (2015) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-015-0370-3 · Cited by 95 · see citing works
Object Detection Based on Sparse Representation and Hough Voting for Optical Remote Sensing Imagery
Yokoya, N., & Iwasaki, A. (2015) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2015.2404578 · Cited by 90 · see citing works
A Novel Vehicle Detection Method With High Resolution Highway Aerial Image
Zheng, Z., Zhou, G., Wang, Y., et al. (2013) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2266131 · Cited by 81 · see citing works
Car detection from high-resolution aerial imagery using multiple features
Shao, W. Q., Yang, W. T., Liu, G., & Liu, J. (2012) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2012.6350403 · Cited by 80 · see citing works
Huang, X., Yuan, W., Li, J., et al. (2016) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2587324 · Cited by 79 · see citing works
A SIFT-SVM method for detecting cars in UAV images
Moranduzzo, T., & Melgani, F. (2012) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2012.6352585 · Cited by 78 · see citing works
A 3D Teacher for Car Detection in Aerial Images
Kluckner, S., Pacher, G., Grabner, H., Bischof, H., & Bauer, J. (2007) · IEEE International Conference on Computer Vision · doi:10.1109/iccv.2007.4408834 · Cited by 64 · see citing works
Chen, Z., Wang, C., Luo, H., Wang, H., Chen, Y., Wen, C., Yu, Y., Cao, L., & Li, J. (2016) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2517826 · Cited by 64 · see citing works
Building Detection Using Enhanced HOG–LBP Features and Region Refinement Processes
Konstantinidis, D., Stathaki, T., Argyriou, V., & Grammalidis, N. (2017) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2016.2602439 · Cited by 58 · see citing works
Automated Vehicle Extraction and Speed Determination From QuickBird Satellite Images
Liu, W., Yamazaki, F., & Vu, T. T. (2010) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2010.2069555 · Cited by 53 · see citing works
Scene Parsing From an MAP Perspective
Li, X., Mou, L., & Lu, X. (2014) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2014.2361489 · Cited by 42 · see citing works
Moving Car Detection and Spectral Restoration in a Single Satellite WorldView-2 Imagery
Bar, D. E., & Raboy, S. (2013) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2013.2253088 · Cited by 22 · see citing works
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Ding, J., Xue, N., Long, Y., Xia, G.-S., & Lu, Q. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00296 · Cited by 1,666 · see citing works
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“The work of [6] proposes a coupled region-based CNNs for aerial vehicle detection.”
BackgroundTackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
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- Authors
- Deng, Z., Sun, H., Zhou, S., Zhao, J., & Zou, H.
- Affiliations
- National University of Defense Technology (CN)
- Shanghai Jiao Tong University (CN)
- Venue
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
- Published
- 2017-08
- Volume
- 10
- Issue
- 8
- Pages
- 3652-3664
- ISSN
- 1939-1404 (ISSN-L)
- 2151-1535
- OpenAlex
- W2613825824
- Cited by
- 195
- References
- 51
- Citations by year
- 2026: 1 · 2025: 11 · 2024: 11 · 2023: 17 · 2022: 27 · 2021: 29 · 2020: 36 · 2019: 37 · 2018: 18 · 2017: 8
- FWCI
- 11.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing (publishedVersion)
- Funders
- National Natural Science Foundation of China (61303186)
- NUDT (B150406)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- vehicle detection, aerial images, R-CNN, small object detection, data augmentation
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